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Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

Author

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  • Jiafu Yin

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Dongmei Zhao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

With the increasing penetration of wind power and demand response integrated into the grid, the combined uncertainties from wind power and demand response have been a challenging concern for system operators. It is necessary to develop an approach to accommodate the combined uncertainties in the source side and load side. In this paper, the fuzzy stochastic conditional value-at-risk criterions are proposed as the risk measure of the combination of both wind power uncertainty and demand response uncertainty. To improve the computational tractability without sacrificing the accuracy, the fuzzy stochastic chance-constrained goal programming is proposed to transfer the fuzzy stochastic conditional value-at-risk to a deterministic equivalent. The operational risk of forecast error under fuzzy stochastic conditional value-at-risk assessment is represented by the shortage of reserve resource, which can be further divided into the load-shedding risk and the wind curtailment risk. To identify different priority levels for the different objective functions, the three-stage day-ahead unit commitment model is proposed through preemptive goal programming, in which the reliability requirement has the priority over the economic operation. Finally, a case simulation is performed on the IEEE 39-bus system to verify the effectiveness and efficiency of the proposed model.

Suggested Citation

  • Jiafu Yin & Dongmei Zhao, 2018. "Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment," Energies, MDPI, vol. 11(2), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:341-:d:130006
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    References listed on IDEAS

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    Cited by:

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    2. Xinyu Sun & Hao Wu & Siqi Guo & Lingwei Zheng, 2022. "Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming," Energies, MDPI, vol. 15(18), pages 1-17, September.
    3. Shahbazitabar, Maryam & Abdi, Hamdi, 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation," Energy, Elsevier, vol. 161(C), pages 308-324.

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